Why Soil Organic Carbon Matters
Soil health sits at the foundation of productive and resilient agricultural systems, and soil organic carbon (SOC) is central to that health. It governs how well soils retain water, cycle nutrients, support microbial life, and ultimately sustain productivity through variable and often challenging conditions. Beyond its agronomic importance, SOC also plays a critical role in climate mitigation as one of the largest carbon sinks on land.
Benefits and factors affecting SOC
Despite its importance, SOC remains difficult to measure and manage effectively at scale. Traditional soil sampling provides the accuracy required for carbon accounting and scientific confidence, but it is inherently limited in coverage and expensive to repeat frequently. This creates a gap between knowing what is happening at specific points and understanding how entire paddocks, properties, and supply chains are performing over time. Bridging that gap is essential for both farmers and investors seeking to make informed, forward-looking decisions.
How D-CAT is Teaming with GXLab to Improve SOC Outcomes
&
Across Australia, and beyond, GXLab is a leader in soil sampling and supporting landholders and agribusinesses on their carbon journey. Their work provides high-confidence, ground-truth data on soil properties — particularly SOC — which underpins carbon accounting, project development, and sustainability strategies.
However, GXLab's customers are increasingly looking for more support in managing their soil health. They want to understand how soil carbon varies across their land, how it is likely to change under current practices, and to be able to prove the impact that new management practices have had on the soil carbon change. This is particularly important for those exploring carbon farming, where both accuracy and scalability are critical.
Teaming with D-CAT allows GXLab to unlock the value of SOC mapping at scale, cost-effectively. The capability also enables them to help clients address management questions for accelerating carbon sequestration in a changing environment through scientific prediction of carbon stocks over time.
Case Study Example: Understanding Soil Carbon Stock & Future Strategies for Improvement
A leading regenerative farmer in New South Wales actively manages their land for healthier soils and improved productivity. Managing large paddocks with variable soil types and grazing histories is challenging and solutions must offer trusted, cost-effective benefit. Detailed mapping of current SOC conditions was sought along with a practical understanding of how those levels could evolve over time given changing conditions and practices.
The question was not only where carbon stood today, but what could be improved, and by how much, given real-world constraints such as climate variability, soil characteristics and operational practices.
Integrating Sampling and Spatial Modelling with D-CAT's Soil Carbon Intelligence
The partnership between GXLab and D-CAT brought together two complementary strengths. GXLab's soil sampling provided accurate baseline measurements against which their machine learning model was calibrated to generate a 10m-resolution map of two complex test paddocks. These provided baseline calibration data for our remote sensing and biogeochemical modelling to extend those insights across space and time.
Using our satellite data services to derive pasture biomass over time at 10m resolution, together with weather data and pasture modelling, these data were applied to the RothC biogeochemical model to simulate carbon dynamics across the paddocks. This approach enables SOC to be monitored from 10-metre pixels through to paddock and supply chain scale. Rather than relying solely on discrete sampling points, the combined solution delivers a spatially complete and temporally dynamic view of soil carbon, alongside the ability to test different management scenarios.
The result is a system that moves beyond measurement into prediction and optimisation, while remaining grounded in scientifically robust methods and real-world data.
Project Activities
Paddocks of interest
The project focused on two key paddocks within the NSW property, selected to reflect contrasting conditions and management histories. GXLab used historic soil sampling data to provide a clear and trusted starting point for their spatial model of the paddocks' SOC a number of years ago.
From there, D-CAT modelled the evolving soil carbon across the two paddocks from the time of the soil sampling to the present day using actual weather data and pasture biomass derived from satellite data using one of its many data services.
Forward projections were then made for carbon stocks assuming typical weather patterns and current management practices and additional scenarios were then explored to understand how improvements in biomass production and overall productivity could influence long-term outcome.
Outputs were generated at high spatial resolution (10m pixels) and aggregated to paddock scale, providing both granular detail and time series projections to inform management decisions going forward.
Key Insights and Results
The modelling revealed clear differences between the two paddocks and the variation of carbon stock within the paddocks. By identifying where interventions would have the greatest impact, the grazier is better equipped to allocate effort and investment effectively.
Another insight from the project was the potential for increasing SOC over time through management practice change. In the example given below, an increase of 15% in annual average pasture biomass with biomass levels then maintained would produce steadily increasing soil carbon levels and make a significant contribution toward lowering annual emissions.
3-year change in SOC from sampling date to SOC estimated for 2026
Projected change in SOC for each paddock over a 25-year period, given a sustained production improvement of 15% through management practice
The modelling also grounded expectations in reality. By incorporating soil type, climate variability, grazing practices, the projections provided achievable pathways for improvement rather than overly optimistic scenarios. This balance of ambition and realism is critical for building confidence in carbon farming strategies.
Value Delivered
For the farmer, the project delivered a clearer understanding of both current conditions and future potential. Decisions around grazing, pasture management and investment can now be informed by evidence, with a clear line of sight to long-term outcomes.
For GXLab, the partnership extends their offering to a scalable intelligence solution that supports ongoing monitoring and scenario planning. This enhances their ability to guide customers through every stage of the carbon journey.
More broadly, the project demonstrates a viable pathway for the industry to combine measurement and modelling in a way that is both scientifically robust and economically practical. It shows how carbon farming can move beyond compliance and reporting to become a tool for improving land productivity and resilience.
Technology Spotlight: Fusion Platform®
At the core of the solution is D-CAT's Fusion Platform®, which enables cost-optimised seamless integration of satellite imagery, climate data, and soil information into a single processing environment. This platform approach allows complex modelling workflows of D-CAT's Soil Carbon Intelligence product to be applied consistently across large geographies, with outputs delivered in formats that are easy to use and integrate into existing systems.
The biogeochemical model underpinning the analysis can be varied to suit locations, cropping systems and compliance, and simulates the movement and transformation of carbon within the soil system, enabling both accurate estimation and forward projection. By combining this scientific rigour with scalable data processing, the platform delivers insights that are both credible and actionable.
Looking Ahead
The collaboration between GXLab and D-CAT establishes a new standard for soil mapping carbon intelligence, moving from point measurements to continuous, landscape-scale understanding. Future work will expand this approach across different farming systems and geographies, incorporating more advanced scenario modelling and deeper integration with carbon markets and sustainability reporting frameworks.
As demand grows for practical, scalable solutions to soil health and carbon management, this combined approach provides a foundation for better decisions, improved outcomes and more resilient agricultural systems.
Conclusion
By bringing together GXLab's expertise in soil sampling and D-CAT's Soil Carbon Intelligence product, this project demonstrated how soil organic carbon can be understood, managed, and improved at scale. The result is a powerful combination of accuracy and coverage, enabling landholders to take confident steps toward healthier soils, stronger productivity, and meaningful participation in the carbon economy.